Towards a Typology of Cross-Channel Dramatic Borrowings: The View from the White Cliffs
Bibliographic record
Abstract
Scholarship on the diverse ways in which early modern English playwrights “translated” French textual material, dramatic and otherwise, has by now accumulated enough specific instances to justify an overview of methods and results. There are few outright translations of French plays, but the field widens considerably when adaptations and appropriations of various kinds are added to the picture. It then becomes possible to identify a variety of intertextual experiences that implicate audiences in issues of genre, religion, and politics. Les recherches sur les différentes approches avec lesquelles les dramaturges anglais des débuts de la modernité ont « traduit » des oeuvres littéraires françaises de tous genres, ont accumulé suffisamment d’études de cas pour permettre un examen global de leurs méthodes et des résultats correspondants. On trouve en réalité peu de traductions intégrales de pièces de théâtre de langue française, mais le corpus s’élargit considérablement lorsqu’on tient compte des adaptations et des appropriations textuelles de différentes sortes. Cela devient alors possible d’identifier une variété d’effets intertextuels engageant spectateurs et lecteurs dans des questions du genre, de la religion et de la politique.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".